In this short talk „On Economics“ Ha-Joon Chang, author of the book „Economics: The User's Guide“, gives a critical wrap-up on the economic discipline – on what is perceived as economics, what are dominant paradigms, the role of numbers and economics in public life. He further elaborates on the importance of heterodox schools of thought. 2014 Level: leicht Ha-Joon Chang on Economics Ha-Joon Chang The RSA This video animates part of the talk “On Economics” by Ha-Joon Chang in which he states that economics is not a science for experts but for everyone. Chang further argues why it is important to take into account different perspectives on economics – he identifies at least nine school of thoughts which all have their strengths and weaknesses and presents examples on free trade and well-being. Chang further elaborates on the difficulties of changing the economic status quo. 2016 Level: leicht Economics Is For Everyone! Ha-Joon Chang The RSA Die Statistik war in ihren Anfängen „Staatszustandswissenschaft“, Fürsten erfassten darin in der Kammer ihr jeweiliges Territorium. Durch die Bevölkerungswissenschaft wandelte sie sich schrittweise zu einer formal-mathematischen Theorie. Parallel dazu setzte sich die Geldökonomie immer mehr als herrschende Form durch und transformierte auch die Vorstellung von den handelnden Subjekten. Durch das Prinzip, nur jeweils Durchschnitte zu erfassen, wurde vor allem in der schottischen Tradition des Liberalismus aus der Ökonomik als „moral science“ eine naturalistische Theorie autonomer Marktprozesse. Die Selbstwahrnehmung der Wirtschaft in Statistik und Ökonomik dient – ungeachtet ihrer vermeintlich neutralen, äußerlichen und formal-mathematischen Struktur – vorwiegend den Interessen einer herrschenden Elite. 2017 Level: mittel Die Selbstwahrnehmung der Wirtschaft: Entstehung und Wandel von Statistik und Ökonomik als Theorie für Eliten Karl-Heinz Brodbeck Institut für Ökonomie und Philosophie Cusanus Hochschule Getting to the policy discussion table is one of the objectives pursued by feminist scholars and advocates. However, some participants in this process have remarked that “you cannot get to the policy discussion table until you have proven that you can crunch the numbers.” 2006 Level: leicht Statistics for Feminists Yana Rodgers Rutgers University, Department of Women’s and Gender Studies This course describes Bayesian statistics in which one s inferences about parameters or hypotheses are updated as evidence accumulates You will learn to use Bayes rule to transform prior probabilities into posterior probabilities and be introduced to the underlying theory and perspective of the Bayesian paradigm The course will apply … Level: leicht Bayesian Statistics Mine Çetinkaya-Rundel; David Banks; Colin Rundel; Merlise A Clyde Duke University This course introduces the main topics in Econometrics by using R statistical software. The relation of themes is comprehensive and includes the basic notions such as linear regression, multiple regression, causal inference, regression discontinuity and instrumental variable. In total, the course covers thirteen chapters that are common in any undergraduate econometrics course. 2020 Level: mittel Introduction to Econometrics with R Florian Oswald, Vincent Viers, Jean-Marc Robin, Pierre Villedieu, Gustave Kenedi Science Po There are three things one can do on this website - 1. Learn 2. Help Teach 3. Sign up for the MOOC. 2013 Level: leicht Econometrics Academy Ani Katchova Econometrics Academy Forecasting is required in many situations. Stocking an inventory may require forecasts of demand months in advance. 2018 Level: mittel Forecasting: principles and practice Rob J Hyndman and George Athanasopoulos OTexts This statistics and data analysis course will introduce you to the essential notions of probability and statistics We will cover techniques in modern data analysis estimation regression and econometrics prediction experimental design randomized control trials and A B testing machine learning and data visualization We will illustrate these concepts with … Level: mittel Data Analysis for Social Scientists Esther Duflo & Sarah Fisher Ellison Massachusetts Institute of Technology